Bibliographic record
Abstract
Joint attention is influential in infant language acquisition, however less is known of the effects of interactive conditions in adult processing and learning.With adults, digital avatar-to-human interactive tasks have resulted in increased image recognition (Kim & Mundy, 2011) and human-to-human experiments have demonstrated joint attention to significantly impact L2 word learning (Hirotani et al., under revision).The aim of the current study was to validate Hirotani et al.'s (under revision) conclusions using a digital interactive paradigm as opposed to as live, face-to-face design.Nine subjects interacted with a video participant in 3 separate learning blocks consisting of 40 picture-pseudoword pairs in four joint attention contexts (responding, initiating, simultaneous and non).Results indicated no performance difference across blocks or conditions and no interaction effect.Further testing is required to determine whether interactive digital environments can also play an implicit role in the effectiveness of joint attention in adult lexical development.I would like to extend my gratitude to the following people for contributing to the success of this project: Dr. Natasha Artemeva for her invigorating support and tenacious dedication to the ALDS cohort; my committee members for a fair evaluation of my progress; the team of Language and Brain Lab Research Assistants and lab members, without whom no data could have been collected; and Dr. Masako Hirotani for granting me use of the equipment in Carleton's Language and Brain Laboratory and for sharing expertise in the design of this project.A special thank you to Kate Carroll for all of her help and support, particularly with project and stimuli design.Thank you mom, dad, grandmas, brothers, Daniella,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".